
\begin{table}[ht!]
\begin{center}
\begin{tabular}{l c c c}
\toprule
 & Black/White & White/Hispanic & White/Asian \\
\midrule
White                      & $0.37^{*}$    & $-0.08$    & $-0.26$   \\
                           & $(0.18)$      & $(0.13)$   & $(0.18)$  \\
Houston                    & $-0.04$       & $0.33$     & $0.11$    \\
                           & $(0.48)$      & $(0.94)$   & $(1.06)$  \\
King County                & $0.10$        & $0.72$     & $0.63$    \\
                           & $(0.58)$      & $(0.98)$   & $(1.05)$  \\
Los Angeles                & $0.71$        & $1.12$     & $1.04$    \\
                           & $(0.46)$      & $(0.92)$   & $(0.99)$  \\
Orlando                    & $0.31$        & $0.14$     & $0.04$    \\
                           & $(0.51)$      & $(1.08)$   & $(1.13)$  \\
San Jose                   & $0.27$        & $0.73$     & $0.73$    \\
                           & $(0.55)$      & $(0.94)$   & $(1.04)$  \\
Seattle                    & $0.72$        & $1.44$     & $1.35$    \\
                           & $(0.51)$      & $(0.93)$   & $(0.99)$  \\
Tucson                     & $0.65$        & $1.50$     & $1.34$    \\
                           & $(0.48)$      & $(0.92)$   & $(0.99)$  \\
Closest Trauma (10s miles) & $0.16$        & $0.27^{*}$ & $0.28$    \\
                           & $(0.08)$      & $(0.11)$   & $(0.21)$  \\
Intercept                  & $-1.65^{***}$ & $-1.79$    & $-1.51$   \\
                           & $(0.44)$      & $(0.92)$   & $(0.99)$  \\
\midrule
pseudo.r.squared           & $0.03$        & $0.03$     & $0.05$    \\
nobs                       & $715$         & $732$      & $279$     \\
AIC                        & $1072.59$     & $1272.88$  & $482.07$  \\
BIC                        & $1118.31$     & $1318.84$  & $518.38$  \\
Log Likelihood             & $-526.30$     & $-626.44$  & $-231.03$ \\
\bottomrule
\multicolumn{4}{l}{\scriptsize{$^{***}p<0.001$; $^{**}p<0.01$; $^{*}p<0.05$}}
\end{tabular}
\caption{Poisson model regression results. Each model is run on a subset of the data to make either a Black/White, Hispanic/White or Asian/White comparison. Cluster-robust standard errors clustered on city. See \citet{zou2004modified} for a discussion of the robust standard error correction when Poisson models are used to calculate relative risks.}
\label{table:regression_all_comparisons_glm}
\end{center}
\end{table}
